Overview

Dataset statistics

Number of variables18
Number of observations12330
Missing cells0
Missing cells (%)0.0%
Duplicate rows125
Duplicate rows (%)1.0%
Total size in memory2.9 MiB
Average record size in memory247.1 B

Variable types

Numeric14
Categorical2
Boolean2

Warnings

Dataset has 125 (1.0%) duplicate rows Duplicates
BounceRates is highly correlated with ExitRatesHigh correlation
ExitRates is highly correlated with BounceRatesHigh correlation
Administrative has 5768 (46.8%) zeros Zeros
Administrative_Duration has 5903 (47.9%) zeros Zeros
Informational has 9699 (78.7%) zeros Zeros
Informational_Duration has 9925 (80.5%) zeros Zeros
ProductRelated_Duration has 755 (6.1%) zeros Zeros
BounceRates has 5518 (44.8%) zeros Zeros
PageValues has 9600 (77.9%) zeros Zeros
SpecialDay has 11079 (89.9%) zeros Zeros

Reproduction

Analysis started2021-02-12 05:17:50.253775
Analysis finished2021-02-12 05:18:50.098224
Duration59.84 seconds
Software versionpandas-profiling v2.10.1
Download configurationconfig.yaml

Variables

Administrative
Real number (ℝ≥0)

ZEROS

Distinct27
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.315166261
Minimum0
Maximum27
Zeros5768
Zeros (%)46.8%
Memory size96.5 KiB
2021-02-11T22:18:50.294510image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile9
Maximum27
Range27
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.321784106
Coefficient of variation (CV)1.434792897
Kurtosis4.701146249
Mean2.315166261
Median Absolute Deviation (MAD)1
Skewness1.960357209
Sum28546
Variance11.03424965
MonotocityNot monotonic
2021-02-11T22:18:50.594828image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
05768
46.8%
11354
 
11.0%
21114
 
9.0%
3915
 
7.4%
4765
 
6.2%
5575
 
4.7%
6432
 
3.5%
7338
 
2.7%
8287
 
2.3%
9225
 
1.8%
Other values (17)557
 
4.5%
ValueCountFrequency (%)
05768
46.8%
11354
 
11.0%
21114
 
9.0%
3915
 
7.4%
4765
 
6.2%
5575
 
4.7%
6432
 
3.5%
7338
 
2.7%
8287
 
2.3%
9225
 
1.8%
ValueCountFrequency (%)
271
 
< 0.1%
261
 
< 0.1%
244
 
< 0.1%
233
 
< 0.1%
224
 
< 0.1%
212
 
< 0.1%
202
 
< 0.1%
196
 
< 0.1%
1812
0.1%
1716
0.1%

Administrative_Duration
Real number (ℝ≥0)

ZEROS

Distinct3335
Distinct (%)27.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.81861054
Minimum0
Maximum3398.75
Zeros5903
Zeros (%)47.9%
Memory size96.5 KiB
2021-02-11T22:18:50.878998image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7.5
Q393.25625
95-th percentile348.2663691
Maximum3398.75
Range3398.75
Interquartile range (IQR)93.25625

Descriptive statistics

Standard deviation176.7791075
Coefficient of variation (CV)2.187356431
Kurtosis50.55673905
Mean80.81861054
Median Absolute Deviation (MAD)7.5
Skewness5.615719019
Sum996493.468
Variance31250.85284
MonotocityNot monotonic
2021-02-11T22:18:51.202543image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05903
47.9%
456
 
0.5%
553
 
0.4%
745
 
0.4%
1142
 
0.3%
641
 
0.3%
1437
 
0.3%
935
 
0.3%
1533
 
0.3%
1032
 
0.3%
Other values (3325)6053
49.1%
ValueCountFrequency (%)
05903
47.9%
1.3333333331
 
< 0.1%
215
 
0.1%
326
 
0.2%
3.54
 
< 0.1%
456
 
0.5%
4.3333333331
 
< 0.1%
4.52
 
< 0.1%
4.751
 
< 0.1%
553
 
0.4%
ValueCountFrequency (%)
3398.751
< 0.1%
2720.51
< 0.1%
2657.3180561
< 0.1%
2629.2539681
< 0.1%
2407.423811
< 0.1%
2156.1666671
< 0.1%
2137.1127451
< 0.1%
2086.751
< 0.1%
2047.2348481
< 0.1%
1951.2791411
< 0.1%

Informational
Real number (ℝ≥0)

ZEROS

Distinct17
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.503568532
Minimum0
Maximum24
Zeros9699
Zeros (%)78.7%
Memory size96.5 KiB
2021-02-11T22:18:51.418391image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum24
Range24
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.270156426
Coefficient of variation (CV)2.522310957
Kurtosis26.93226626
Mean0.503568532
Median Absolute Deviation (MAD)0
Skewness4.03646376
Sum6209
Variance1.613297346
MonotocityNot monotonic
2021-02-11T22:18:51.645160image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
09699
78.7%
11041
 
8.4%
2728
 
5.9%
3380
 
3.1%
4222
 
1.8%
599
 
0.8%
678
 
0.6%
736
 
0.3%
915
 
0.1%
814
 
0.1%
Other values (7)18
 
0.1%
ValueCountFrequency (%)
09699
78.7%
11041
 
8.4%
2728
 
5.9%
3380
 
3.1%
4222
 
1.8%
599
 
0.8%
678
 
0.6%
736
 
0.3%
814
 
0.1%
915
 
0.1%
ValueCountFrequency (%)
241
 
< 0.1%
161
 
< 0.1%
142
 
< 0.1%
131
 
< 0.1%
125
 
< 0.1%
111
 
< 0.1%
107
 
0.1%
915
0.1%
814
 
0.1%
736
0.3%

Informational_Duration
Real number (ℝ≥0)

ZEROS

Distinct1258
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.47239793
Minimum0
Maximum2549.375
Zeros9925
Zeros (%)80.5%
Memory size96.5 KiB
2021-02-11T22:18:51.855639image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile195
Maximum2549.375
Range2549.375
Interquartile range (IQR)0

Descriptive statistics

Standard deviation140.7492944
Coefficient of variation (CV)4.082956304
Kurtosis76.31685309
Mean34.47239793
Median Absolute Deviation (MAD)0
Skewness7.579184716
Sum425044.6664
Variance19810.36388
MonotocityNot monotonic
2021-02-11T22:18:52.102821image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
09925
80.5%
933
 
0.3%
626
 
0.2%
1026
 
0.2%
726
 
0.2%
1223
 
0.2%
1323
 
0.2%
822
 
0.2%
1622
 
0.2%
1121
 
0.2%
Other values (1248)2183
 
17.7%
ValueCountFrequency (%)
09925
80.5%
13
 
< 0.1%
1.51
 
< 0.1%
211
 
0.1%
2.51
 
< 0.1%
316
 
0.1%
3.51
 
< 0.1%
417
 
0.1%
518
 
0.1%
5.53
 
< 0.1%
ValueCountFrequency (%)
2549.3751
< 0.1%
2256.9166671
< 0.1%
2252.0333331
< 0.1%
2195.31
< 0.1%
2166.51
< 0.1%
2050.4333331
< 0.1%
1949.1666671
< 0.1%
1830.51
< 0.1%
1779.1666671
< 0.1%
17781
< 0.1%

ProductRelated
Real number (ℝ≥0)

Distinct311
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.73146796
Minimum0
Maximum705
Zeros38
Zeros (%)0.3%
Memory size96.5 KiB
2021-02-11T22:18:52.363542image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median18
Q338
95-th percentile109
Maximum705
Range705
Interquartile range (IQR)31

Descriptive statistics

Standard deviation44.4755033
Coefficient of variation (CV)1.401621361
Kurtosis31.21170665
Mean31.73146796
Median Absolute Deviation (MAD)13
Skewness4.341516416
Sum391249
Variance1978.070394
MonotocityNot monotonic
2021-02-11T22:18:52.952515image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1622
 
5.0%
2465
 
3.8%
3458
 
3.7%
4404
 
3.3%
6396
 
3.2%
7391
 
3.2%
5382
 
3.1%
8370
 
3.0%
10330
 
2.7%
9317
 
2.6%
Other values (301)8195
66.5%
ValueCountFrequency (%)
038
 
0.3%
1622
5.0%
2465
3.8%
3458
3.7%
4404
3.3%
5382
3.1%
6396
3.2%
7391
3.2%
8370
3.0%
9317
2.6%
ValueCountFrequency (%)
7051
< 0.1%
6861
< 0.1%
5841
< 0.1%
5341
< 0.1%
5181
< 0.1%
5171
< 0.1%
5011
< 0.1%
4861
< 0.1%
4701
< 0.1%
4491
< 0.1%

ProductRelated_Duration
Real number (ℝ≥0)

ZEROS

Distinct9551
Distinct (%)77.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1194.74622
Minimum0
Maximum63973.52223
Zeros755
Zeros (%)6.1%
Memory size96.5 KiB
2021-02-11T22:18:53.207061image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1184.1375
median598.9369047
Q31464.157214
95-th percentile4300.289077
Maximum63973.52223
Range63973.52223
Interquartile range (IQR)1280.019714

Descriptive statistics

Standard deviation1913.669288
Coefficient of variation (CV)1.601737052
Kurtosis137.1741637
Mean1194.74622
Median Absolute Deviation (MAD)500.9369047
Skewness7.263227683
Sum14731220.89
Variance3662130.143
MonotocityNot monotonic
2021-02-11T22:18:53.502573image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0755
 
6.1%
1721
 
0.2%
817
 
0.1%
1117
 
0.1%
1516
 
0.1%
1915
 
0.1%
2215
 
0.1%
1215
 
0.1%
714
 
0.1%
1314
 
0.1%
Other values (9541)11431
92.7%
ValueCountFrequency (%)
0755
6.1%
0.51
 
< 0.1%
12
 
< 0.1%
2.3333333331
 
< 0.1%
2.6666666671
 
< 0.1%
35
 
< 0.1%
410
 
0.1%
513
 
0.1%
5.3333333331
 
< 0.1%
65
 
< 0.1%
ValueCountFrequency (%)
63973.522231
< 0.1%
43171.233381
< 0.1%
29970.465971
< 0.1%
27009.859431
< 0.1%
24844.15621
< 0.1%
23888.811
< 0.1%
23342.082051
< 0.1%
23050.104141
< 0.1%
21857.046481
< 0.1%
21672.244251
< 0.1%

BounceRates
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1872
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02219138047
Minimum0
Maximum0.2
Zeros5518
Zeros (%)44.8%
Memory size96.5 KiB
2021-02-11T22:18:53.717724image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.0031124675
Q30.0168125585
95-th percentile0.2
Maximum0.2
Range0.2
Interquartile range (IQR)0.0168125585

Descriptive statistics

Standard deviation0.04848832181
Coefficient of variation (CV)2.185007006
Kurtosis7.723159431
Mean0.02219138047
Median Absolute Deviation (MAD)0.0031124675
Skewness2.947855267
Sum273.6197212
Variance0.002351117352
MonotocityNot monotonic
2021-02-11T22:18:54.051268image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05518
44.8%
0.2700
 
5.7%
0.066666667134
 
1.1%
0.028571429115
 
0.9%
0.05113
 
0.9%
0.033333333101
 
0.8%
0.025100
 
0.8%
0.01666666799
 
0.8%
0.198
 
0.8%
0.0496
 
0.8%
Other values (1862)5256
42.6%
ValueCountFrequency (%)
05518
44.8%
2.73 × 1051
 
< 0.1%
3.35 × 1051
 
< 0.1%
3.83 × 1051
 
< 0.1%
3.94 × 1051
 
< 0.1%
7.09 × 1051
 
< 0.1%
7.27 × 1051
 
< 0.1%
7.5 × 1051
 
< 0.1%
8.01 × 1051
 
< 0.1%
8.08 × 1051
 
< 0.1%
ValueCountFrequency (%)
0.2700
5.7%
0.1833333331
 
< 0.1%
0.185
 
< 0.1%
0.1769230771
 
< 0.1%
0.1751
 
< 0.1%
0.1666666674
 
< 0.1%
0.1642857141
 
< 0.1%
0.1642307691
 
< 0.1%
0.1619047621
 
< 0.1%
0.163
 
< 0.1%

ExitRates
Real number (ℝ≥0)

HIGH CORRELATION

Distinct4777
Distinct (%)38.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04307279777
Minimum0
Maximum0.2
Zeros76
Zeros (%)0.6%
Memory size96.5 KiB
2021-02-11T22:18:54.362844image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.004567568
Q10.014285714
median0.0251564025
Q30.05
95-th percentile0.2
Maximum0.2
Range0.2
Interquartile range (IQR)0.035714286

Descriptive statistics

Standard deviation0.04859654055
Coefficient of variation (CV)1.128242024
Kurtosis4.017034553
Mean0.04307279777
Median Absolute Deviation (MAD)0.0141725795
Skewness2.148789
Sum531.0875965
Variance0.002361623754
MonotocityNot monotonic
2021-02-11T22:18:54.644562image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2710
 
5.8%
0.1338
 
2.7%
0.05329
 
2.7%
0.033333333291
 
2.4%
0.066666667267
 
2.2%
0.025224
 
1.8%
0.04214
 
1.7%
0.016666667181
 
1.5%
0.02167
 
1.4%
0.022222222152
 
1.2%
Other values (4767)9457
76.7%
ValueCountFrequency (%)
076
0.6%
0.0001755931
 
< 0.1%
0.0002504381
 
< 0.1%
0.0002621231
 
< 0.1%
0.0002631581
 
< 0.1%
0.0002923981
 
< 0.1%
0.0004098361
 
< 0.1%
0.0004464291
 
< 0.1%
0.0004683841
 
< 0.1%
0.0004807691
 
< 0.1%
ValueCountFrequency (%)
0.2710
5.8%
0.1923076921
 
< 0.1%
0.1888888892
 
< 0.1%
0.1866666674
 
< 0.1%
0.1833333332
 
< 0.1%
0.1818181821
 
< 0.1%
0.180341881
 
< 0.1%
0.183
 
< 0.1%
0.1777777785
 
< 0.1%
0.1756
 
< 0.1%

PageValues
Real number (ℝ≥0)

ZEROS

Distinct2704
Distinct (%)21.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.889257863
Minimum0
Maximum361.7637419
Zeros9600
Zeros (%)77.9%
Memory size96.5 KiB
2021-02-11T22:18:54.930627image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile38.16052828
Maximum361.7637419
Range361.7637419
Interquartile range (IQR)0

Descriptive statistics

Standard deviation18.56843661
Coefficient of variation (CV)3.152933195
Kurtosis65.63569361
Mean5.889257863
Median Absolute Deviation (MAD)0
Skewness6.382964249
Sum72614.54945
Variance344.7868381
MonotocityNot monotonic
2021-02-11T22:18:55.222692image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
09600
77.9%
53.9886
 
< 0.1%
42.293067523
 
< 0.1%
16.15855822
 
< 0.1%
12.558857142
 
< 0.1%
59.9882
 
< 0.1%
10.999018442
 
< 0.1%
40.40144812
 
< 0.1%
34.039975362
 
< 0.1%
21.21126552
 
< 0.1%
Other values (2694)2707
 
22.0%
ValueCountFrequency (%)
09600
77.9%
0.0380345421
 
< 0.1%
0.0670495461
 
< 0.1%
0.0935469491
 
< 0.1%
0.0986214031
 
< 0.1%
0.1206999141
 
< 0.1%
0.1296768931
 
< 0.1%
0.1318370131
 
< 0.1%
0.1392006231
 
< 0.1%
0.1506504981
 
< 0.1%
ValueCountFrequency (%)
361.76374191
< 0.1%
360.95338391
< 0.1%
287.95379281
< 0.1%
270.78469311
< 0.1%
261.49128571
< 0.1%
258.54987321
< 0.1%
255.56915791
< 0.1%
254.60715791
< 0.1%
246.75859021
< 0.1%
239.981
< 0.1%

SpecialDay
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06142741281
Minimum0
Maximum1
Zeros11079
Zeros (%)89.9%
Memory size96.5 KiB
2021-02-11T22:18:55.452383image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.6
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1989172732
Coefficient of variation (CV)3.238249245
Kurtosis9.91365887
Mean0.06142741281
Median Absolute Deviation (MAD)0
Skewness3.302666747
Sum757.4
Variance0.03956808156
MonotocityNot monotonic
2021-02-11T22:18:55.669875image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
011079
89.9%
0.6351
 
2.8%
0.8325
 
2.6%
0.4243
 
2.0%
0.2178
 
1.4%
1154
 
1.2%
ValueCountFrequency (%)
011079
89.9%
0.2178
 
1.4%
0.4243
 
2.0%
0.6351
 
2.8%
0.8325
 
2.6%
1154
 
1.2%
ValueCountFrequency (%)
1154
 
1.2%
0.8325
 
2.6%
0.6351
 
2.8%
0.4243
 
2.0%
0.2178
 
1.4%
011079
89.9%

Month
Categorical

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size722.9 KiB
May
3364 
Nov
2998 
Mar
1907 
Dec
1727 
Oct
549 
Other values (5)
1785 

Length

Max length4
Median length3
Mean length3.023357664
Min length3

Characters and Unicode

Total characters37278
Distinct characters22
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFeb
2nd rowFeb
3rd rowFeb
4th rowFeb
5th rowFeb
ValueCountFrequency (%)
May3364
27.3%
Nov2998
24.3%
Mar1907
15.5%
Dec1727
14.0%
Oct549
 
4.5%
Sep448
 
3.6%
Aug433
 
3.5%
Jul432
 
3.5%
June288
 
2.3%
Feb184
 
1.5%
2021-02-11T22:18:56.193437image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-11T22:18:56.386693image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
may3364
27.3%
nov2998
24.3%
mar1907
15.5%
dec1727
14.0%
oct549
 
4.5%
sep448
 
3.6%
aug433
 
3.5%
jul432
 
3.5%
june288
 
2.3%
feb184
 
1.5%

Most occurring characters

ValueCountFrequency (%)
M5271
14.1%
a5271
14.1%
y3364
9.0%
N2998
8.0%
o2998
8.0%
v2998
8.0%
e2647
7.1%
c2276
 
6.1%
r1907
 
5.1%
D1727
 
4.6%
Other values (12)5821
15.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter24948
66.9%
Uppercase Letter12330
33.1%

Most frequent character per category

ValueCountFrequency (%)
a5271
21.1%
y3364
13.5%
o2998
12.0%
v2998
12.0%
e2647
10.6%
c2276
9.1%
r1907
 
7.6%
u1153
 
4.6%
t549
 
2.2%
p448
 
1.8%
Other values (4)1337
 
5.4%
ValueCountFrequency (%)
M5271
42.7%
N2998
24.3%
D1727
 
14.0%
J720
 
5.8%
O549
 
4.5%
S448
 
3.6%
A433
 
3.5%
F184
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Latin37278
100.0%

Most frequent character per script

ValueCountFrequency (%)
M5271
14.1%
a5271
14.1%
y3364
9.0%
N2998
8.0%
o2998
8.0%
v2998
8.0%
e2647
7.1%
c2276
 
6.1%
r1907
 
5.1%
D1727
 
4.6%
Other values (12)5821
15.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII37278
100.0%

Most frequent character per block

ValueCountFrequency (%)
M5271
14.1%
a5271
14.1%
y3364
9.0%
N2998
8.0%
o2998
8.0%
v2998
8.0%
e2647
7.1%
c2276
 
6.1%
r1907
 
5.1%
D1727
 
4.6%
Other values (12)5821
15.6%

OperatingSystems
Real number (ℝ≥0)

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.124006488
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Memory size96.5 KiB
2021-02-11T22:18:56.618691image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile3
Maximum8
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9113248287
Coefficient of variation (CV)0.4290593432
Kurtosis10.45684261
Mean2.124006488
Median Absolute Deviation (MAD)0
Skewness2.066285042
Sum26189
Variance0.8305129434
MonotocityNot monotonic
2021-02-11T22:18:57.073620image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
26601
53.5%
12585
 
21.0%
32555
 
20.7%
4478
 
3.9%
879
 
0.6%
619
 
0.2%
77
 
0.1%
56
 
< 0.1%
ValueCountFrequency (%)
12585
 
21.0%
26601
53.5%
32555
 
20.7%
4478
 
3.9%
56
 
< 0.1%
619
 
0.2%
77
 
0.1%
879
 
0.6%
ValueCountFrequency (%)
879
 
0.6%
77
 
0.1%
619
 
0.2%
56
 
< 0.1%
4478
 
3.9%
32555
 
20.7%
26601
53.5%
12585
 
21.0%

Browser
Real number (ℝ≥0)

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.357096513
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Memory size96.5 KiB
2021-02-11T22:18:57.282571image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile5
Maximum13
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.717276676
Coefficient of variation (CV)0.7285559443
Kurtosis12.74673269
Mean2.357096513
Median Absolute Deviation (MAD)0
Skewness3.242349611
Sum29063
Variance2.94903918
MonotocityNot monotonic
2021-02-11T22:18:57.512947image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
27961
64.6%
12462
 
20.0%
4736
 
6.0%
5467
 
3.8%
6174
 
1.4%
10163
 
1.3%
8135
 
1.1%
3105
 
0.9%
1361
 
0.5%
749
 
0.4%
Other values (3)17
 
0.1%
ValueCountFrequency (%)
12462
 
20.0%
27961
64.6%
3105
 
0.9%
4736
 
6.0%
5467
 
3.8%
6174
 
1.4%
749
 
0.4%
8135
 
1.1%
91
 
< 0.1%
10163
 
1.3%
ValueCountFrequency (%)
1361
 
0.5%
1210
 
0.1%
116
 
< 0.1%
10163
 
1.3%
91
 
< 0.1%
8135
 
1.1%
749
 
0.4%
6174
 
1.4%
5467
3.8%
4736
6.0%

Region
Real number (ℝ≥0)

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.147364152
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Memory size96.5 KiB
2021-02-11T22:18:57.706946image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q34
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.401591237
Coefficient of variation (CV)0.7630484178
Kurtosis-0.1486803001
Mean3.147364152
Median Absolute Deviation (MAD)2
Skewness0.9835491595
Sum38807
Variance5.767640468
MonotocityNot monotonic
2021-02-11T22:18:57.937410image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
14780
38.8%
32403
19.5%
41182
 
9.6%
21136
 
9.2%
6805
 
6.5%
7761
 
6.2%
9511
 
4.1%
8434
 
3.5%
5318
 
2.6%
ValueCountFrequency (%)
14780
38.8%
21136
 
9.2%
32403
19.5%
41182
 
9.6%
5318
 
2.6%
6805
 
6.5%
7761
 
6.2%
8434
 
3.5%
9511
 
4.1%
ValueCountFrequency (%)
9511
 
4.1%
8434
 
3.5%
7761
 
6.2%
6805
 
6.5%
5318
 
2.6%
41182
 
9.6%
32403
19.5%
21136
 
9.2%
14780
38.8%

TrafficType
Real number (ℝ≥0)

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.069586375
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Memory size96.5 KiB
2021-02-11T22:18:58.310043image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q34
95-th percentile13
Maximum20
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.02516916
Coefficient of variation (CV)0.9890855703
Kurtosis3.479710597
Mean4.069586375
Median Absolute Deviation (MAD)1
Skewness1.962986732
Sum50178
Variance16.20198677
MonotocityNot monotonic
2021-02-11T22:18:58.529550image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
23913
31.7%
12451
19.9%
32052
16.6%
41069
 
8.7%
13738
 
6.0%
10450
 
3.6%
6444
 
3.6%
8343
 
2.8%
5260
 
2.1%
11247
 
2.0%
Other values (10)363
 
2.9%
ValueCountFrequency (%)
12451
19.9%
23913
31.7%
32052
16.6%
41069
 
8.7%
5260
 
2.1%
6444
 
3.6%
740
 
0.3%
8343
 
2.8%
942
 
0.3%
10450
 
3.6%
ValueCountFrequency (%)
20198
 
1.6%
1917
 
0.1%
1810
 
0.1%
171
 
< 0.1%
163
 
< 0.1%
1538
 
0.3%
1413
 
0.1%
13738
6.0%
121
 
< 0.1%
11247
 
2.0%

VisitorType
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size880.2 KiB
Returning_Visitor
10551 
New_Visitor
1694 
Other
 
85

Length

Max length17
Median length17
Mean length16.09294404
Min length5

Characters and Unicode

Total characters198426
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReturning_Visitor
2nd rowReturning_Visitor
3rd rowReturning_Visitor
4th rowReturning_Visitor
5th rowReturning_Visitor
ValueCountFrequency (%)
Returning_Visitor10551
85.6%
New_Visitor1694
 
13.7%
Other85
 
0.7%
2021-02-11T22:18:59.146652image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-11T22:18:59.484721image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
returning_visitor10551
85.6%
new_visitor1694
 
13.7%
other85
 
0.7%

Most occurring characters

ValueCountFrequency (%)
i35041
17.7%
t22881
11.5%
r22881
11.5%
n21102
10.6%
e12330
 
6.2%
_12245
 
6.2%
V12245
 
6.2%
s12245
 
6.2%
o12245
 
6.2%
R10551
 
5.3%
Other values (6)24660
12.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter161606
81.4%
Uppercase Letter24575
 
12.4%
Connector Punctuation12245
 
6.2%

Most frequent character per category

ValueCountFrequency (%)
i35041
21.7%
t22881
14.2%
r22881
14.2%
n21102
13.1%
e12330
 
7.6%
s12245
 
7.6%
o12245
 
7.6%
u10551
 
6.5%
g10551
 
6.5%
w1694
 
1.0%
ValueCountFrequency (%)
V12245
49.8%
R10551
42.9%
N1694
 
6.9%
O85
 
0.3%
ValueCountFrequency (%)
_12245
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin186181
93.8%
Common12245
 
6.2%

Most frequent character per script

ValueCountFrequency (%)
i35041
18.8%
t22881
12.3%
r22881
12.3%
n21102
11.3%
e12330
 
6.6%
V12245
 
6.6%
s12245
 
6.6%
o12245
 
6.6%
R10551
 
5.7%
u10551
 
5.7%
Other values (5)14109
7.6%
ValueCountFrequency (%)
_12245
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII198426
100.0%

Most frequent character per block

ValueCountFrequency (%)
i35041
17.7%
t22881
11.5%
r22881
11.5%
n21102
10.6%
e12330
 
6.2%
_12245
 
6.2%
V12245
 
6.2%
s12245
 
6.2%
o12245
 
6.2%
R10551
 
5.3%
Other values (6)24660
12.4%

Weekend
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.2 KiB
False
9462 
True
2868 
ValueCountFrequency (%)
False9462
76.7%
True2868
 
23.3%
2021-02-11T22:18:59.735425image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Revenue
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.2 KiB
False
10422 
True
1908 
ValueCountFrequency (%)
False10422
84.5%
True1908
 
15.5%
2021-02-11T22:18:59.927589image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Interactions

2021-02-11T22:17:59.428504image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:17:59.723643image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:17:59.938373image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:00.172228image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:00.502760image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:00.704713image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:00.904694image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:01.264832image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:01.544744image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:01.754918image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:01.974849image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:02.203954image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:02.392815image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:02.592716image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:02.886685image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:03.287908image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:03.524715image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:03.804032image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:04.018041image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:04.337385image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:04.564677image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:04.774699image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:04.994000image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:05.282991image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:05.544706image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:05.747569image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:05.942610image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:06.124712image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:06.316673image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:06.510051image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:06.710311image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:06.902843image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:07.092685image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:07.422707image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:07.669899image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:07.875916image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:08.115276image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:08.400605image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:08.603649image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:08.838666image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:09.142617image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:09.373252image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:09.746727image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:09.945061image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:10.154694image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:10.355131image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:10.634131image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:10.857570image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:11.042703image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:11.256789image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:11.552792image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:11.934653image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:12.218992image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:12.426934image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:12.636774image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:12.863053image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:13.091224image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:13.502957image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:13.854656image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:14.188663image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:14.610731image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:14.962837image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:15.382110image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:15.699978image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:15.975076image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:16.312597image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:16.546776image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:16.770318image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:16.972649image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:17.356253image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:17.556273image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:17.817649image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:18.054909image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:18.252828image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:18.448936image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:18.647736image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:18.874981image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:19.190002image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:19.517080image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:19.841408image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:20.075943image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:20.287849image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:20.615365image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:20.882651image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:21.135445image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:21.422757image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:21.672786image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:21.867669image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:22.195888image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:22.566536image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:22.910954image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:23.175004image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:23.468458image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:23.703601image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:23.947935image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:24.394248image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:24.653634image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:24.891523image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:25.156079image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:25.472862image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:25.684653image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:26.014744image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:26.288991image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:26.504657image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:26.752827image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:26.957831image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:27.235671image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:27.438956image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:27.759329image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:28.045521image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:28.294666image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:28.595818image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:28.857582image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:29.122520image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:29.317647image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:29.512889image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:29.804696image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:30.169877image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:30.456575image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:30.705531image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:31.009389image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:31.333952image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:31.829807image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:32.064672image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:32.277601image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:32.544626image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:32.843880image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:33.126682image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:33.394862image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:33.763921image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:34.153556image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:34.716793image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:35.304699image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:35.758387image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:36.163154image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:36.459466image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:36.674620image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:37.020450image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:37.379932image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:37.610928image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:37.994372image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:38.242932image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:38.454735image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:38.737520image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:39.004606image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:39.224126image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:39.453852image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:39.756874image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:39.976393image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:40.424644image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:40.644644image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:40.842626image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:41.072770image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:41.312618image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:41.504602image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:41.728726image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:42.055672image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:42.406395image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:42.684673image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:42.939261image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:43.167900image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:43.428972image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:43.679265image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:43.966439image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:44.424911image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:44.725915image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:44.954401image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:45.167499image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:45.403805image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:45.722511image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:45.950674image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:46.194928image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:46.394589image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:46.594590image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:46.798572image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:47.012908image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:47.515450image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:47.873785image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:48.259806image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:48.499410image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:48.702657image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-11T22:18:48.892599image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-02-11T22:19:00.174765image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-02-11T22:19:00.626334image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-02-11T22:19:01.277317image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-02-11T22:19:01.952512image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-02-11T22:19:02.475901image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-02-11T22:18:49.343541image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-11T22:18:49.862721image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

AdministrativeAdministrative_DurationInformationalInformational_DurationProductRelatedProductRelated_DurationBounceRatesExitRatesPageValuesSpecialDayMonthOperatingSystemsBrowserRegionTrafficTypeVisitorTypeWeekendRevenue
000.000.010.0000000.2000000.2000000.00.0Feb1111Returning_VisitorFalseFalse
100.000.0264.0000000.0000000.1000000.00.0Feb2212Returning_VisitorFalseFalse
200.000.010.0000000.2000000.2000000.00.0Feb4193Returning_VisitorFalseFalse
300.000.022.6666670.0500000.1400000.00.0Feb3224Returning_VisitorFalseFalse
400.000.010627.5000000.0200000.0500000.00.0Feb3314Returning_VisitorTrueFalse
500.000.019154.2166670.0157890.0245610.00.0Feb2213Returning_VisitorFalseFalse
600.000.010.0000000.2000000.2000000.00.4Feb2433Returning_VisitorFalseFalse
710.000.000.0000000.2000000.2000000.00.0Feb1215Returning_VisitorTrueFalse
800.000.0237.0000000.0000000.1000000.00.8Feb2223Returning_VisitorFalseFalse
900.000.03738.0000000.0000000.0222220.00.4Feb2412Returning_VisitorFalseFalse

Last rows

AdministrativeAdministrative_DurationInformationalInformational_DurationProductRelatedProductRelated_DurationBounceRatesExitRatesPageValuesSpecialDayMonthOperatingSystemsBrowserRegionTrafficTypeVisitorTypeWeekendRevenue
1232000.0000.08143.5833330.0142860.0500000.0000000.0Nov2231Returning_VisitorFalseFalse
1232100.0000.060.0000000.2000000.2000000.0000000.0Nov1841Returning_VisitorFalseFalse
12322676.2500.0221075.2500000.0000000.0041670.0000000.0Dec2242Returning_VisitorFalseFalse
12323264.7500.0441157.9761900.0000000.0139530.0000000.0Nov22110Returning_VisitorFalseFalse
1232400.0010.016503.0000000.0000000.0376470.0000000.0Nov2211Returning_VisitorFalseFalse
123253145.0000.0531783.7916670.0071430.02903112.2417170.0Dec4611Returning_VisitorTrueFalse
1232600.0000.05465.7500000.0000000.0213330.0000000.0Nov3218Returning_VisitorTrueFalse
1232700.0000.06184.2500000.0833330.0866670.0000000.0Nov32113Returning_VisitorTrueFalse
12328475.0000.015346.0000000.0000000.0210530.0000000.0Nov22311Returning_VisitorFalseFalse
1232900.0000.0321.2500000.0000000.0666670.0000000.0Nov3212New_VisitorTrueFalse

Duplicate rows

Most frequent

AdministrativeAdministrative_DurationInformationalInformational_DurationProductRelatedProductRelated_DurationBounceRatesExitRatesPageValuesSpecialDayMonthOperatingSystemsBrowserRegionTrafficTypeVisitorTypeWeekendRevenuecount
2600.000.010.00.20.20.00.0Mar2211Returning_VisitorFalseFalse14
3600.000.010.00.20.20.00.0Mar3231Returning_VisitorFalseFalse7
4400.000.010.00.20.20.00.0May2213Returning_VisitorFalseFalse7
3800.000.010.00.20.20.00.0May1113Returning_VisitorFalseFalse6
1300.000.010.00.20.20.00.0Dec813920OtherFalseFalse5
3400.000.010.00.20.20.00.0Mar3211Returning_VisitorFalseFalse4
4100.000.010.00.20.20.00.0May1143Returning_VisitorFalseFalse4
6000.000.010.00.20.20.00.0Nov2211Returning_VisitorFalseFalse4
000.000.010.00.20.20.00.0Dec1111Returning_VisitorTrueFalse3
300.000.010.00.20.20.00.0Dec1141Returning_VisitorTrueFalse3